How to Deploy Machine Learning Models on Mobile and Embedded Devices

Thanks to libraries such as Pandas, scikit-learn, and Matplotlib, it is relatively easy to start exploring datasets and make some first predictions using simple Machine Learning (ML) algorithms in Python. Although, to make these trained models useful in the real world, it is necessary to make them available to make predictions on either the Web or Portable devices. In two of my previous articles, I explained how to create and deploy a simple Machine Learning model using Heroku/Flask and Tensorflow.js. Today, I will instead explain to you how to deploy Machine Learning models on Smartphones and Embedded Devices using TensorFlow Lite. TensorFlow Lite is a platform developed by Google to train Machine Learning models on mobile, IoT (Interned of Things) and embedded devices.